以安全正确的方式使用 RandomForestClassifier 的 predict_proba() 函数 [英] Using the predict_proba() function of RandomForestClassifier in the safe and right way

问题描述

我正在使用 Scikit-learn.有时我需要标签/类的概率而不是标签/类本身.与其将垃圾邮件/非垃圾邮件作为电子邮件标签，我希望仅举例:给定电子邮件是垃圾邮件的概率为 0.78.

I'm using Scikit-learn. Sometimes I need to have the probabilities of labels/classes instead of the labels/classes themselves. Instead of having Spam/Not Spam as labels of emails, I wish to have only for example: 0.78 probability a given email is Spam.

为此，我将 predict_proba() 与 RandomForestClassifier 一起使用，如下所示:

For such purpose, I'm using predict_proba() with RandomForestClassifier as following:

clf = RandomForestClassifier(n_estimators=10, max_depth=None,
    min_samples_split=1, random_state=0)
scores = cross_val_score(clf, X, y)
print(scores.mean())

classifier = clf.fit(X,y)
predictions = classifier.predict_proba(Xtest)
print(predictions)

我得到了这些结果:

 [ 0.4  0.6]
 [ 0.1  0.9]
 [ 0.2  0.8]
 [ 0.7  0.3]
 [ 0.3  0.7]
 [ 0.3  0.7]
 [ 0.7  0.3]
 [ 0.4  0.6]

其中第二列用于类:垃圾邮件.但是，我对结果有两个主要问题，我对此没有信心.第一个问题是结果代表了标签的概率，而不受我的数据大小的影响?第二个问题是结果仅显示一位数字，这在某些情况下不是很具体，其中 0.701 概率与 0.708 的概率非常不同.例如，有没有办法获得下一个 5 位数字?

Where the second column is for class: Spam. However, I have two main issues with the results about which I am not confident. The first issue is that the results represent the probabilities of the labels without being affected by the size of my data? The second issue is that the results show only one digit which is not very specific in some cases where the 0.701 probability is very different from 0.708. Is there any way to get the next 5 digit for example?

推荐答案

1. 我的结果中有不止一位数字，您确定这不是由于您的数据集造成的吗?(例如，使用非常小的数据集会产生简单的决策树，因此会产生简单"的概率).否则它可能只显示一位数字，但尝试打印predictions[0,0].

我不确定您所说的概率不受我的数据大小影响"是什么意思.如果您担心您不想预测，例如，太多垃圾邮件，通常所做的是使用阈值 t 这样您预测 1 if proba(label==1) >t.通过这种方式，您可以使用阈值来平衡您的预测，例如限制垃圾邮件的全球概率.如果您想全局分析您的模型，我们通常会计算接收器操作特征 (ROC) 曲线的曲线下面积 (AUC)(请参阅维基百科文章 此处).基本上，ROC 曲线是根据阈值 t 对您的预测的描述.

I am not sure to understand what you mean by "the probabilities aren't affected by the size of my data". If your concern is that you don't want to predict, eg, too many spams, what is usually done is to use a threshold t such that you predict 1 if proba(label==1) > t. This way you can use the threshold to balance your predictions, for example to limit the global probabilty of spams. And if you want to globally analyse your model, we usually compute the Area under the curve (AUC) of the Receiver operating characteristic (ROC) curve (see wikipedia article here). Basically the ROC curve is a description of your predictions depending on the threshold t.

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